...a computational framework [1] that shows how large networks of spiking neurons can store and transform analog signals for sensory processing, motor control, and statistical inference. The resulting computational systems differ from traditional artificial neural networks that are focused on the highly nonlinear properties of individual neurons, and are more in line with modern Bayesian systems. The brain is more like an analog computer than a digital one; more like a Bayesian inference machine than a symbolic one.

"The ability to find one's way depends on neural algorithms that integrate information about place, distance and direction, but the implementation of these operations in cortical microcircuits is poorly understood. Here we show that the dorsocaudal medial entorhinal cortex (dMEC) contains a directionally oriented, topographically organized neural map of the spatial environment. Its key unit is the 'grid cell', which is activated whenever the animal's position coincides with any vertex of a regular grid of equilateral triangles spanning the surface of the environment. Grids of neighbouring cells share a common orientation and spacing, but their vertex locations (their phases) differ. The spacing and size of individual fields increase from dorsal to ventral dMEC. The map is anchored to external landmarks, but persists in their absence, suggesting that grid cells may be part of a generalized, path-integration-based map of the spatial environment."